A convolutional neural network for sleep stage scoring from raw single-channel EEG

Biomedical Signal Processing and Control - Tập 42 - Trang 107-114 - 2018
Arnaud Sors1,2, Stéphane Bonnet1,2, Sébastien Mirek3, Laurent Vercueil4,2, Jean‐François Payen5,2
1CEA, LETI, MINATEC Campus, 17 rue des Martyrs, F-38054 Grenoble, France
2Univ. Grenoble Alpes, F-38000 Grenoble, France
3Dijon University Hospital, Dpt. Anesth. and Crit. Care, 14 rue Paul Gaffarel, F-21079 Dijon, France
4Grenoble University Hospital, Dpt. Exploration Fonctionnelle du Système Nerveux, Avenue du Maquis du Grésivaudan, F-38700 La Tronche, France
5Grenoble University Hospital, Dpt. Anesth. and Crit. Care, Avenue Maquis du Grésivaudan, F-38700 La Tronche, France

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Tài liệu tham khảo

Rechtschaffen, 1971

Berry, 2012

Stepnowsky, 2013, Scoring accuracy of automated sleep staging from a bipolar electroocular recording compared to manual scoring by multiple raters, Sleep Med., 14, 1199, 10.1016/j.sleep.2013.04.022

Wang, 2015, Evaluation of an automated single-channel sleep staging algorithm, Nat. Sci. Sleep, 7, 101

Radha, 2014, Comparison of feature and classifier algorithms for online automatic sleep staging based on a single EEG signal, Annual International Conference of the IEEE Engineering in Medicine and Biology Society, 2014, 1876, 10.1109/EMBC.2014.6943976

Fraiwan, 2012, Automated sleep stage identification system based on time-frequency analysis of a single EEG channel and random forest classifier, Comput. Methods Progr. Biomed., 108, 10, 10.1016/j.cmpb.2011.11.005

Koley, 2012, An ensemble system for automatic sleep stage classification using single channel EEG signal, Comput. Biol. Med., 42, 1186, 10.1016/j.compbiomed.2012.09.012

Tsinalis, 2016, Automatic sleep stage scoring using time-frequency analysis and stacked sparse autoencoders, Ann. Biomed. Eng., 44, 1587, 10.1007/s10439-015-1444-y

Liang, 2012, Automatic stage scoring of single-channel sleep EEG by using multiscale entropy and autoregressive models, IEEE Trans. Instrum. Meas., 61, 1649, 10.1109/TIM.2012.2187242

Zhu, 2014, Analysis and classification of sleep stages based on difference visibility graphs from a single-channel EEG signal, IEEE J. Biomed. Health Inf., 18, 1813, 10.1109/JBHI.2014.2303991

Hassan, 2016, Computer-aided sleep staging using complete ensemble empirical mode decomposition with adaptive noise and bootstrap aggregating, Biomed. Signal Process. Control, 24, 1, 10.1016/j.bspc.2015.09.002

Hassan, 2016, A decision support system for automatic sleep staging from EEG signals using tunable Q-factor wavelet transform and spectral features, J. Neurosci. Methods, 271, 107, 10.1016/j.jneumeth.2016.07.012

Sharma, 2017, Automatic sleep stages classification based on iterative filtering of electroencephalogram signals, Neural Comput. Appl., 1

Hsu, 2013, Automatic sleep stage recurrent neural classifier using energy features of EEG signals, Neurocomputing, 104, 105, 10.1016/j.neucom.2012.11.003

Tsinalis, 2016

Supratak, 2017

Lecun, 1998, Gradient-based learning applied to document recognition, Proc. IEEE, 86, 2278, 10.1109/5.726791

Krizhevsky, 2012, ImageNet classification with deep convolutional neural networks, Adv. Neural Inf. Process. Syst., 1097

Collobert, 2008, A unified architecture for natural language processing: deep neural networks with multitask learning, 160

van den Oord, 2013, Deep content-based music recommendation, 2643

Cecotti, 2011, Convolutional neural networks for p300 detection with application to brain–computer interfaces, IEEE Trans. Pattern Anal. Mach. Intell., 33, 433, 10.1109/TPAMI.2010.125

Manor, 2015, Convolutional neural network for multi-category rapid serial visual presentation BCI, Front. Comput. Neurosci., 9, 10.3389/fncom.2015.00146

Tang, 2017, Single-trial EEG classification of motor imagery using deep convolutional neural networks, Optik, 130, 11, 10.1016/j.ijleo.2016.10.117

Bevilacqua, 2014, A novel BCI-SSVEP based approach for control of walking in virtual environment using a convolutional neural network, 4121

Page, 2016, Wearable seizure detection using convolutional neural networks with transfer learning, 1086

Hajinoroozi, 2015, Prediction of driver's drowsy and alert states from EEG signals with deep learning, 493

Drouin-Picaro, 2016, Using deep neural networks for natural saccade classification from electroencephalograms, 1

Quan, 1997, The sleep heart health study: design, rationale, and methods, Sleep, 20, 1077

Sleep Data – National Sleep Research Resource – NSRR, https://sleepdata.org/.

Xu, 2015

Kingma, 2014

Erhan, 2009, vol. 1341, 3

Zeiler, 2010, Deconvolutional networks, 2528

Abadi, 2016

Dietterich, 2000, Ensemble methods in machine learning, Mult. Classif. Syst., 1857, 1, 10.1007/3-540-45014-9_1

Huang, 2016, Learning deep representation for imbalanced classification, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 5375

Goldberger, 2000, PhysioBank, PhysioToolkit, and PhysioNet components of a new research resource for complex physiologic signals, Circulation, 101, e215, 10.1161/01.CIR.101.23.e215

Hassan, 2016, Automatic sleep scoring using statistical features in the EMD domain and ensemble methods, Biocybern. Biomed. Eng., 36, 248, 10.1016/j.bbe.2015.11.001

Hassan, 2017, Automated identification of sleep states from EEG signals by means of ensemble empirical mode decomposition and random under sampling boosting, Comput. Methods Progr. Biomed., 140, 201, 10.1016/j.cmpb.2016.12.015

Esteva, 2017, Dermatologist-level classification of skin cancer with deep neural networks, Nature, 542, 115, 10.1038/nature21056

Gulshan, 2016, Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs, JAMA, 316, 2402, 10.1001/jama.2016.17216

Chambon, 2017

He, 2016, Deep residual learning for image recognition, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 770

Chollet, 2016